| 摘要 |
<p>Systems and methods of the present solution provide a more optimal solution by dynamically and automatically reacting to changing network workload. A system that starts slowly, either by just examining traffic passively or by doing sub-optimal acceleration can learn over time, how many peer WAN optimizers are being serviced by an appliance, how much traffic is coming from each peer WAN optimizers, and the type of traffic being seen. Knowledge from this learning can serve to provide a better or improved baseline for the configuration of an appliance. In some embodiments, based on resources (e.g., CPU, Memory, Disk), the system from this knowledge may determine how many WAN optimization instances should be used and of what size, and how the load should be distributed across the instances of the WAN optimizer.</p> |
